The relationship of heart rate variability measures with trait emotional intelligence in the presence of acute mental stress
Bibliographic record
Abstract
Abstract Objective: Emotional intelligence (EI) is essential for effective stress management and may influence cardiac responses. This study seeks to investigate the relationship between EI and heart rate variability (HRV) due to limited physiological data, contributing valuable insights into this unexplored connection and its potential impact on overall well-being. Results: In a study of 55 participants, mainly undergraduate students (45.5%) and single (70.9%), females constituted 56.4% of the sample. The highest well-being score was 5.187 (Cronbach’s alpha = 0.76). One-way repeated measures ANOVA showed significant HRV differences across time points. Median HF at stress induction was 0.193 (IQR: 0.160–0.217), significantly decreasing from baseline (Z = -4.926, p < 0.001). LF/HF ratio increased at T2 (M = 0.579, p < 0.001), with SD2/SD1 ratio rising to 2.171. Pairwise comparisons indicated differences between baseline and stress induction (M = -0.018, p < 0.001), and stress induction and post-induction periods (M = 0.174, p = 0.032). SDNN correlated positively at T1 (r = 0.321, p = 0.020) and T3 (r = 0.280, p = 0.045). pNN50% correlated positively at T1 (r = 0.332, p = 0.020), while stress index showed negative correlations at T1 (r = -0.318, p = 0.023) and T3 (r = -0.337, p = 0.012). Sociability negatively correlated with HRV measures (r = -0.407, p = 0.002), indicating autonomic nervous system activity links.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".